Introduction to TensorFlow

TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud. See the sections below to get started.


Learn the foundation of TensorFlow with tutorials for beginners and experts to help you create your next machine learning project.

For JavaScript

Use TensorFlow.js to create new machine learning models and deploy existing models with JavaScript.

For Mobile & IoT

Run inference with TensorFlow Lite on mobile and embedded devices like Android, iOS, Edge TPU, and Raspberry Pi.

For Production

Deploy a production-ready ML pipeline for training and inference using TensorFlow Extended (TFX).

TensorFlow ecosystem

TensorFlow provides a collection of workflows to develop and train models using Python or JavaScript, and to easily deploy in the cloud, on-prem, in the browser, or on-device no matter what language you use.

Load & preprocess data
Build, train & reuse models
Build TensorFlow Input Pipelines
The API enables you to build complex input pipelines from simple, reusable pieces.
Build and train models using Keras
tf.keras is a high-level API to build and train models. It supports TensorFlow-specific functionality, such as eager execution, pipelines, and estimators.
Deploy using Python
Deploy on a mobile or edge device, in browser, or at scale using TensorFlow Serving.
Use pretrained TensorFlow.js, TensorFlow or TFLite models and run them on the web or other JS platforms.
TensorFlow Lite
Deploy on mobile or embedded devices, like Android, iOS, and Raspberry Pi
Read the developer guide and pick a new model or retrain an existing one, convert it to a compressed file, load it on an edge device, and then optimize it.
Validate input data with TF Data Validation
See how to use TFX components to analyze and transform your data before you even train a model.
Feature engineering with TF Transform
Learn how to define a preprocessing function that transforms raw data into the data used to train a machine learning model, and see how the Apache Beam implementation is used to transform data by converting the preprocessing function into a Beam pipeline.
Modeling and training
Learn how to train your models in a TFX pipeline as a managed process.
Understanding model performance with TF model analysis
See how TensorFlow Model Analysis allows you to perform model evaluations in the TFX pipeline and visualize the results in a Jupyter notebook.
Serve models with a REST API with TF Serving
Learn how TensorFlow Serving allows you to deploy new algorithms and experiments while keeping the same server architecture and APIs.
TensorBoard is a tool to visualize training and results
With TensorBoard you can track experiment metrics like loss and accuracy, visualize the model graph, project embeddings to a lower dimensional space, and more.
TensorFlow Hub
TensorFlow Hub is an extensive library of existing models
TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models called modules.

Looking to expand your ML knowledge?

TensorFlow is easier to use with a basic understanding of machine learning principles and core concepts. Learn and apply fundamental machine learning practices to develop your skills.

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